Wykresy w tabeli danych

// Define a DataTable using a JavaScript array with a column property header.
var dataTable = [
  [
    {label: 'State', role: 'domain', type: 'string'},
    {label: 'Population', role: 'data', type: 'number'},
    {label: 'Pop. annotation', role: 'annotation', type: 'string'}
  ],
  ['CA', 37253956, '37.2e6'],
  ['NY', 19378102, '19.3e6'],
  ['IL', 12830632, '12.8e6'],
  ['MI', 9883640, '9.8e6'],
  ['OR', 3831074, '3.8e6']
];

// Define the chart and print it to the console.
var chart = ui.Chart(dataTable).setChartType('ColumnChart').setOptions({
  title: 'State Population (US census, 2010)',
  legend: {position: 'none'},
  hAxis: {title: 'State', titleTextStyle: {italic: false, bold: true}},
  vAxis: {title: 'Population', titleTextStyle: {italic: false, bold: true}},
  colors: ['1d6b99']
});
print(chart);

// Import the example feature collection and subset the forest feature.
var forest = ee.FeatureCollection('projects/google/charts_feature_example')
                 .filter(ee.Filter.eq('label', 'Forest'));

// Load MODIS vegetation indices data and subset a decade of images.
var vegIndices = ee.ImageCollection('MODIS/061/MOD13A1')
                     .filter(ee.Filter.date('2010-01-01', '2020-01-01'))
                     .select(['NDVI', 'EVI']);

// Define a function to format an image timestamp as a JavaScript Date string.
function formatDate(img) {
  var millis = img.date().millis().format();
  return ee.String('Date(').cat(millis).cat(')');
}

// Build a feature collection where each feature has a property that represents
// a DataFrame row.
var reductionTable = vegIndices.map(function(img) {
  // Reduce the image to the mean of pixels intersecting the forest ecoregion.
  var stat = img.reduceRegion(
      {reducer: ee.Reducer.mean(), geometry: forest, scale: 500});

  // Extract the reduction results along with the image date.
  var date = formatDate(img);   // x-axis values.
  var evi = stat.get('EVI');    // y-axis series 1 values.
  var ndvi = stat.get('NDVI');  // y-axis series 2 values.

  // Make a list of observation attributes to define a row in the DataTable.
  var row = ee.List([date, evi, ndvi]);

  // Return the row as a property of an ee.Feature.
  return ee.Feature(null, {'row': row});
});

// Aggregate the 'row' property from all features in the new feature collection
// to make a server-side 2-D list (DataTable).
var dataTableServer = reductionTable.aggregate_array('row');

// Define column names and properties for the DataTable. The order should
// correspond to the order in the construction of the 'row' property above.
var columnHeader = ee.List([[
  {label: 'Date', role: 'domain', type: 'date'},
  {label: 'EVI', role: 'data', type: 'number'},
  {label: 'NDVI', role: 'data', type: 'number'}
]]);

// Concatenate the column header to the table.
dataTableServer = columnHeader.cat(dataTableServer);

// Use 'evaluate' to transfer the server-side table to the client, define the
// chart and print it to the console.
dataTableServer.evaluate(function(dataTableClient) {
  var chart = ui.Chart(dataTableClient).setOptions({
    title: 'Average Vegetation Index Value by Date for Forest',
    hAxis: {
      title: 'Date',
      titleTextStyle: {italic: false, bold: true},
    },
    vAxis: {
      title: 'Vegetation index (x1e4)',
      titleTextStyle: {italic: false, bold: true}
    },
    lineWidth: 5,
    colors: ['e37d05', '1d6b99'],
    curveType: 'function'
  });
  print(chart);
});

// Define a point to extract an NDVI time series for.
var geometry = ee.Geometry.Point([-121.679, 36.479]);

// Define a band of interest (NDVI), import the MODIS vegetation index dataset,
// and select the band.
var band = 'NDVI';
var ndviCol = ee.ImageCollection('MODIS/006/MOD13Q1').select(band);

// Map over the collection to add a day of year (doy) property to each image.
ndviCol = ndviCol.map(function(img) {
  var doy = ee.Date(img.get('system:time_start')).getRelative('day', 'year');
  // Add 8 to day of year number so that the doy label represents the middle of
  // the 16-day MODIS NDVI composite.
  return img.set('doy', ee.Number(doy).add(8));
});

// Join all coincident day of year observations into a set of image collections.
var distinctDOY = ndviCol.filterDate('2013-01-01', '2014-01-01');
var filter = ee.Filter.equals({leftField: 'doy', rightField: 'doy'});
var join = ee.Join.saveAll('doy_matches');
var joinCol = ee.ImageCollection(join.apply(distinctDOY, ndviCol, filter));

// Calculate the absolute range, interquartile range, and median for the set
// of images composing each coincident doy observation group. The result is
// an image collection with an image representative per unique doy observation
// with bands that describe the 0, 25, 50, 75, 100 percentiles for the set of
// coincident doy images.
var comp = ee.ImageCollection(joinCol.map(function(img) {
  var doyCol = ee.ImageCollection.fromImages(img.get('doy_matches'));

  return doyCol
      .reduce(ee.Reducer.percentile(
          [0, 25, 50, 75, 100], ['p0', 'p25', 'p50', 'p75', 'p100']))
      .set({'doy': img.get('doy')});
}));

// Extract the inter-annual NDVI doy percentile statistics for the
// point of interest per unique doy representative. The result is
// is a feature collection where each feature is a doy representative that
// contains a property (row) describing the respective inter-annual NDVI
// variance, formatted as a list of values.
var reductionTable = comp.map(function(img) {
  var stats = ee.Dictionary(img.reduceRegion(
      {reducer: ee.Reducer.first(), geometry: geometry, scale: 250}));

  // Order the percentile reduction elements according to how you want columns
  // in the DataTable arranged (x-axis values need to be first).
  var row = ee.List([
    img.get('doy'),            // x-axis, day of year.
    stats.get(band + '_p50'),  // y-axis, median.
    stats.get(band + '_p0'),   // y-axis, min interval.
    stats.get(band + '_p25'),  // y-axis, 1st quartile interval.
    stats.get(band + '_p75'),  // y-axis, 3rd quartile interval.
    stats.get(band + '_p100')  // y-axis, max interval.
  ]);

  // Return the row as a property of an ee.Feature.
  return ee.Feature(null, {row: row});
});

// Aggregate the 'row' properties to make a server-side 2-D array (DataTable).
var dataTableServer = reductionTable.aggregate_array('row');

// Define column names and properties for the DataTable. The order should
// correspond to the order in the construction of the 'row' property above.
var columnHeader = ee.List([[
  {label: 'Day of year', role: 'domain'},
  {label: 'Median', role: 'data'},
  {label: 'p0', role: 'interval'},
  {label: 'p25', role: 'interval'},
  {label: 'p75', role: 'interval'},
  {label: 'p100', role: 'interval'}
]]);

// Concatenate the column header to the table.
dataTableServer = columnHeader.cat(dataTableServer);

// Use 'evaluate' to transfer the server-side table to the client, define the
// chart and print it to the console.
dataTableServer.evaluate(function(dataTableClient) {
  var chart = ui.Chart(dataTableClient).setChartType('LineChart').setOptions({
    title: 'Annual NDVI Time Series with Inter-Annual Variance',
    intervals: {style: 'area'},
    hAxis: {
      title: 'Day of year',
      titleTextStyle: {italic: false, bold: true},
    },
    vAxis: {title: 'NDVI (x1e4)', titleTextStyle: {italic: false, bold: true}},
    colors: ['0f8755'],
    legend: {position: 'none'}
  });
  print(chart);
});